We note that even though from the previous section the STOCHX algorithm was the
most intelligent, it was the worst performing algorithm in the large warehouse, even
lower than CRW (it still meets the minimum collective performance, however, per the
scenario specification). Previous work [1]} has shown that the
STOCHX algorithm outperforms the STOCHM, DPO, and CRW algorithms in dual source
foraging (fig:ds-foraging), an environment much more conducive to the
emergence of strong traffic patterns to increase efficiency. The random foraging
environment studied here is much more adverse in terms of facilitating such
emergence, leading to the observed lower performance. This difference demonstrates
the necessity of accurate modeling and thorough analysis when designing SR systems,
and the interaction between scenario characteristics and swarm dynamics (e.g., the
object distribution model can result in strong orthogonality between scalability and
emergent self-organization), and the importance of matching algorithms to
environments in which they are naturally well-suited.
